It　has　been　recongised　that　subjects　may　change　their　gait　pattern　when　walking　in　different　environments.　This　paper　investigated　the　impact　of　walking　environments　on　gait　monitoring　and　analysis.　A　tri-axial　accelerometer　attached　to　subject＇s　lower　back　was　used　to　record　gait　pattern　while　walking　in　5　different　urban　environments　(quiet　street,　busy　street,　cobbled　street,　dark　street　and　checkerboard　floor).　Forty-one　young　students　participated　the　experiment.　For　each　trial,　a　total　of　33　gait　features　were　extracted,　of　which　11　were　derived　from　the　entire　walking　trial　and　22　were　computed　for　each　stride　cycle.　Statistics　analysis　showed　that　7　out　of　11　features　extracted　from　each　trial　were　significantly　different　across　the　five　environments.　The　obtained　results　suggested　that　different　environments　have　various　impacts　on　gait　features　extracted　from　accelerometer　data.　To　further　access　the　impact,　a　multi-layer　perceptrons　based　hierarchical　classification　approach　was　proposed　to　discriminate　stride　cycles　taken　from　different　walking　environments.　The　classification　accuracy　of　each　level　ranged　from　98.26%　to　65.62%　with　the　discrimination　of　walking　in　quiet　environment　achieving　the　best　performance.